[R-sig-ME] how to look at the effect of a variable I need to control for

glenda mendieta glendamendieta at gmail.com
Fri Nov 18 17:13:19 CET 2011


Dear list members:

a while ago I made a consultation about the use of GLMM's that can be 
found here:
https://stat.ethz.ch/pipermail/r-sig-mixed-models/2011q4/006873.html
I know there is a lot going on in the list for every consultation to be 
answered, but, this time I have "simpler" question:

I have a doubt concerning a factor I want to see the effect from, but I 
also need to control for.
My data consists on:
5 *census* in 10 years, each time we inspect for abundance of species 
(*spp*) occurring on different individuals of a unique species of *tree* 
(plots).
-census: 5 levels, as Fixed effect, since I want to see the effect of 
time in the change of pres.abs or abundance of species
-trees: ~89 to 113, each individual tree inspected, as Ran.Eff., since I 
hoped to control for temporal correlation, as we revise the same trees 
every census
-spp: 89, number per species of epiphytes growing on the trees
-abs.pres: absence presence data of species growing on trees per census 
(derived form count data), as ResVar
-avail.surface: surface in m2 per tree per census, as FE

in the following model, and with the above mentioned data, I would like 
to test for the effect of time and surface availability on colonization 
(absence/presence). My problem is that I don't know how to combine the 
fact that the data are temporally correlated and control for that but 
still look at the effect of time in absence and presence of species.
I tried placing time as a centered continuous variable as fixed effect 
"c.census", and then again, as random effect, but as a factor in 
(census|tree) or would be enough as: (1|tree), since the trees are the 
ones being inspected every time?

glmm.all<-glmer(abs.pres~c.census*avail.surface+ (census|tree), 
data=db.e_St, family=binomial(link=logit))

I would very much appreciate a hint on this since I got stuck with it 
and can not seem to find my way around it.

thank you very much for your time in advance,

glenda mendieta-leiva
PhD candidate
University of Oldenburg, Germany
Smithsonian Tropical research institute




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